Maybe it's worth thinking about whether something along the lines of @jamesheathers &amp; @sTeamTraen's work in psych can be applied here (although I think that mostly works on integer data)
https://t.co/0cnbf4OSIl
GRIM test: https://t.co/ZPSLtHPcrV
SPRITE: https://t.co/rAJnWtXBl5

If you wish to host #RShiny apps on Code Ocean, this capsule provides a template and Readme for running one: https://t.co/M0iFX9JTBI
The underlying Rsprite code is courtesy of @sTeamTraen, which you can read more about here: https://t.co/DKXUl16rRO

@wolfvanpaemel Good question. Yeah there's a formula for the 'full canopy' i.e. the line created using sets of (in this case) 0 and 400. This sort of error corresponds to the green box in Figure 1B.
https://t.co/WO3JaJJtEt

The latest version of SPRITE up at https://t.co/BRTOfHBYMr (thanks to @lakens for the update and continued hosting).
For more info, our preprint is here https://t.co/Rgf1PsH12S, tech explanation here https://t.co/zj20S6LzwF, and demo here https://t.co/pkEn7tqVCP

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.

Abstract

Scientific publications have not traditionally been accompanied by data, either during the peer review process or when published. Concern has arisen that the literature in many fields may contain inaccuracies or errors that cannot be detected without inspecting the original data. Here, we introduce SPRITE (Sample Parameter Reconstruction via Interative TEchniques), a heuristic method for reconstructing plausible samples from descriptive statistics of granular data, allowing reviewers, editors, readers, and future researchers to gain insights into the possible distributions of item values in the original data set. This paper presents the principles of operation of SPRITE, as well as worked examples of its practical use for error detection in real published work. Full source code for three software implementations of SPRITE (in MATLAB, R, and Python) and two web-based implementations requiring no local installation (1, 2) are available for readers.

Author Comment

This pre-print manuscript version (1.0) serves as a longer exposition of the technique; we expect at present it will be reformatted and condensed for future publication.

Additional Information

Competing Interests

JA operates omnesres.com, oncolnc.org, and prepubmed.org. TvdZ has a blog entitled “The Skeptical Scientist” at timvanderzee.com. NJLB has a blog at sTeamTraen.blogspot.com that hosts advertising; his earnings in 2017 were €26.03.

Author Contributions

James A Heathers conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.

Jordan Anaya conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.

Tim van der Zee conceived and designed the experiments, performed the experiments, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.

Nicholas JL Brown conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.

Funding

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